
# import necessary libraries and modules

import warnings
warnings.filterwarnings("ignore")

import numpy as np
import pandas as pd
import io
import requests
import matplotlib.pyplot as plt

pd.set_option('display.max_columns', None)

import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline 
%config InlineBackend.figure_format = 'retina'
plt.style.use("seaborn")


# read dataset from our local directory

stroke_df = pd.read_csv('Phase1_Group10.csv')


# check the number of observations and features

stroke_df.shape

(5110, 12)


# print 10 random observations

stroke_df.sample(10, random_state=99)


# Create a table to explain features that will be used in this project 

from tabulate import tabulate

table = [['Name','Data Type','Units','Description'],
         ['Gender','Nominal categorical','NA','Indicates whether the individual is male or female.'],
         ['Age','Numeric','Years','Represents the age of the individual.'],
         ['Hypertension','Binary','NA','Indicates whether the individual has high blood pressure or not.'],
         ['Heart Disease','Binary','NA','Indicates whether the individual has heart disease or not.'],
         ['Marital Status','Binary','NA','Indicates whether the individual has been married or not.'],
         ['Work Type','Nominal categorical','NA','Describes the type of work the individual is engaged in, which may reflect lifestyle factors.'],
         ['Residence Type','Binary','NA','Indicates whether the individual resides in an urban or rural area.'],
         ['Average Glucose Level','Numeric','mg/dL','Represents the average blood sugar level in the individual\'s blood.'],
         ['BMI','Numeric','kg/m^2','A measure of body fat based on height and weight.'],
         ['Smoking Status','Nominal categorical','NA','Indicates the individual\'s smoking habits.'],
         ['Stroke','Binary','NA','A binary indicator for stroke occurrence, specifying whether the individual has had a stroke or not.']]

print(tabulate(table, headers='firstrow', tablefmt='fancy_grid', maxcolwidths=[20, 20, None,60]))

╒═════════════════╤═════════════════════╤═════════╤══════════════════════════════════════════════════════════════╕
│ Name            │ Data Type           │ Units   │ Description                                                  │
╞═════════════════╪═════════════════════╪═════════╪══════════════════════════════════════════════════════════════╡
│ Gender          │ Nominal categorical │ NA      │ Indicates whether the individual is male or female.          │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Age             │ Numeric             │ Years   │ Represents the age of the individual.                        │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Hypertension    │ Binary              │ NA      │ Indicates whether the individual has high blood pressure or  │
│                 │                     │         │ not.                                                         │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Heart Disease   │ Binary              │ NA      │ Indicates whether the individual has heart disease or not.   │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Marital Status  │ Binary              │ NA      │ Indicates whether the individual has been married or not.    │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Work Type       │ Nominal categorical │ NA      │ Describes the type of work the individual is engaged in,     │
│                 │                     │         │ which may reflect lifestyle factors.                         │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Residence Type  │ Binary              │ NA      │ Indicates whether the individual resides in an urban or      │
│                 │                     │         │ rural area.                                                  │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Average Glucose │ Numeric             │ mg/dL   │ Represents the average blood sugar level in the individual's │
│ Level           │                     │         │ blood.                                                       │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ BMI             │ Numeric             │ kg/m^2  │ A measure of body fat based on height and weight.            │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Smoking Status  │ Nominal categorical │ NA      │ Indicates the individual's smoking habits.                   │
├─────────────────┼─────────────────────┼─────────┼──────────────────────────────────────────────────────────────┤
│ Stroke          │ Binary              │ NA      │ A binary indicator for stroke occurrence, specifying whether │
│                 │                     │         │ the individual has had a stroke or not.                      │
╘═════════════════╧═════════════════════╧═════════╧══════════════════════════════════════════════════════════════╛


stroke_df.columns

Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',
       'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',
       'smoking_status', 'stroke'],
      dtype='object')


stroke_df.columns = stroke_df.columns.str.lower().str.strip()
stroke_df.columns

Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',
       'work_type', 'residence_type', 'avg_glucose_level', 'bmi',
       'smoking_status', 'stroke'],
      dtype='object')


stroke_df = stroke_df.drop(columns='id')
stroke_df.columns

Index(['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',
       'work_type', 'residence_type', 'avg_glucose_level', 'bmi',
       'smoking_status', 'stroke'],
      dtype='object')


stroke_df = stroke_df.rename(columns={'ever_married': 'marital_status'})
stroke_df.columns

Index(['gender', 'age', 'hypertension', 'heart_disease', 'marital_status',
       'work_type', 'residence_type', 'avg_glucose_level', 'bmi',
       'smoking_status', 'stroke'],
      dtype='object')


print(f"Shape of the dataset = {stroke_df.shape} \n")
print(f"Data types are below where 'object' indicates a string type: ")
print(stroke_df.dtypes)

Shape of the dataset = (5110, 11) 

Data types are below where 'object' indicates a string type: 
gender                object
age                  float64
hypertension           int64
heart_disease          int64
marital_status        object
work_type             object
residence_type        object
avg_glucose_level    float64
bmi                  float64
smoking_status        object
stroke                 int64
dtype: object


stroke_df = stroke_df.applymap(lambda x: x.lower() if isinstance(x, str) else x)
stroke_df.sample(10, random_state=99)


from IPython.display import display, HTML
display(HTML('<b>Table 1: Summary of Categorical Features</b>'))
stroke_df.describe(include='object').T


# To see all unique values for categorical data types

categoricalColumns = stroke_df.columns[stroke_df.dtypes==object].tolist()

for col in categoricalColumns:
    print('Unique values for ' + col + '\n')
    print(stroke_df[col].value_counts())
    print('')

Unique values for gender

female    2994
male      2115
other        1
Name: gender, dtype: int64

Unique values for marital_status

yes    3353
no     1757
Name: marital_status, dtype: int64

Unique values for work_type

private          2925
self-employed     819
children          687
govt_job          657
never_worked       22
Name: work_type, dtype: int64

Unique values for residence_type

urban    2596
rural    2514
Name: residence_type, dtype: int64

Unique values for smoking_status

never smoked       1892
unknown            1544
formerly smoked     885
smokes              789
Name: smoking_status, dtype: int64


# drop the row that contain other in gender column

stroke_df = stroke_df.drop(stroke_df[stroke_df['gender'] == 'other'].index)


# check if they are not people who works in roles specific to children, such as childcare or education
# filter them by their age under 18, the result tell us that they are all under 18

stroke_df.loc[(stroke_df['work_type'] == 'children') & (stroke_df['age'] >= 18), 'age']

Series([], Name: age, dtype: float64)


# replace children with never_worked

stroke_df['work_type'] = stroke_df['work_type'].replace('children', 'never_worked')


# check all unique values for categorical data types again

categoricalColumns = stroke_df.columns[stroke_df.dtypes==object].tolist()

for col in categoricalColumns:
    print('Unique values for ' + col + '\n')
    print(stroke_df[col].value_counts())
    print('')

Unique values for gender

female    2994
male      2115
Name: gender, dtype: int64

Unique values for marital_status

yes    3353
no     1756
Name: marital_status, dtype: int64

Unique values for work_type

private          2924
self-employed     819
never_worked      709
govt_job          657
Name: work_type, dtype: int64

Unique values for residence_type

urban    2596
rural    2513
Name: residence_type, dtype: int64

Unique values for smoking_status

never smoked       1892
unknown            1544
formerly smoked     884
smokes              789
Name: smoking_status, dtype: int64


from IPython.display import display, HTML
display(HTML('<b>Table 2: Summary of numerical features</b>'))
stroke_df.describe(include=['int64','float64']).T


binaryColumns = ['hypertension','heart_disease','stroke']
for bin_col in binaryColumns:
    print('Unique values for ' + bin_col + '\n')
    print(stroke_df[bin_col].unique())
    print('')

Unique values for hypertension

[0 1]

Unique values for heart_disease

[1 0]

Unique values for stroke

[1 0]


# Count missing values in each column

print(f"\nNumber of missing values for each column/feature:\n")
print(stroke_df.isnull().sum())

Number of missing values for each column/feature:

gender                 0
age                    0
hypertension           0
heart_disease          0
marital_status         0
work_type              0
residence_type         0
avg_glucose_level      0
bmi                  201
smoking_status         0
stroke                 0
dtype: int64


# Drop all rows with missing values/NaN

stroke_df = stroke_df.dropna()


from IPython.display import display, HTML
display(HTML('<b>Table 2: Summary of Numerical Features</b>'))
stroke_df.describe(include=['int64','float64']).T


stroke_df.shape

(4908, 11)


# initiate a function to plot bar chart

def create_barchart_with_percentage(df, feature, ax=None, figNum = None):
    if ax is None:
        ax = plt.gca()  # Get the current axes if ax is not provided
    
    sns.set_palette(sns.color_palette("Set2"))
    
    ax = sns.countplot(x=df[feature], order=df[feature].value_counts(ascending=False).index, ax=ax)
    
    # Calculate absolute and relative values
    abs_values = df[feature].value_counts(ascending=False)
    rel_values = df[feature].value_counts(ascending=False, normalize=True).values * 100
    lbls = [f'{p[0]} ({p[1]:.0f}%)' for p in zip(abs_values, rel_values)]
    if figNum == None:
        ax.bar_label(container=ax.containers[0], labels=lbls)
        ax.set_title(f'Bar Chart of {feature.capitalize()}\n')
    else: 
        ax.bar_label(container=ax.containers[0], labels=lbls)
        ax.set_title(f'Figure {figNum}: Bar Chart of {feature.capitalize()}\n')
    
    return ax


# Adjust the figure size

plt.figure(figsize=(15, 5))

plt.subplot(1, 3, 1)
create_barchart_with_percentage(stroke_df, 'gender')

plt.subplot(1, 3, 2)
create_barchart_with_percentage(stroke_df, 'marital_status')

plt.subplot(1, 3, 3)
create_barchart_with_percentage(stroke_df, 'residence_type')

# Adjust subplot layout to prevent overlapping

plt.tight_layout()
plt.suptitle('Figure 1: Comparison of Demographic Distributions', y=1.1, fontsize=20)
plt.show()


def create_KDE(df, feature, ax=None):
    if ax is None:
        ax = plt.gca()  # Get the current axes if ax is not provided

    sns.kdeplot(df[feature], shade=True, ax=ax)
    
    # Calculate the mean
    mean_value = np.mean(df[feature])
    
    # Overlay a mean line
    ax.axvline(mean_value, color='red', linestyle='--')
    ax.text(mean_value, ax.get_ylim()[1]*0.8, f'Mean: {mean_value:.2f}', color='red', ha='left')
    
    ax.set_xlabel(f'{feature.capitalize()}')
    ax.set_ylabel('Density')
    ax.set_title(f'Kernel Density of {feature.capitalize()} with Mean Line\n')

    return ax


# Create subplots
plt.figure(figsize=(15, 5))

plt.subplot(1, 3, 1)
create_KDE(stroke_df, 'age')

plt.subplot(1, 3, 2)
create_KDE(stroke_df, 'bmi')

plt.subplot(1, 3, 3)
create_KDE(stroke_df, 'avg_glucose_level')

# Adjust subplot layout to prevent overlapping
plt.tight_layout()
plt.suptitle('Figure 2: Comparison of Continuous Features', y=1.1, fontsize=20)
plt.show()


create_barchart_with_percentage(stroke_df,'smoking_status',figNum = 3)

<AxesSubplot:title={'center':'Figure 3: Bar Chart of Smoking_status\n'}, xlabel='smoking_status', ylabel='count'>


stroke_df_copied = stroke_df.copy()
stroke_df_copied['stroke'] = stroke_df['heart_disease'].map({0: 'No', 1: 'Yes'})
create_barchart_with_percentage(stroke_df_copied,'stroke', figNum=4)

<AxesSubplot:title={'center':'Figure 4: Bar Chart of Stroke\n'}, xlabel='stroke', ylabel='count'>


stroke_df_copied = stroke_df.copy()
stroke_df_copied['stroke'] = stroke_df['stroke'].map({0: 'No', 1: 'Yes'})

ax = sns.countplot(x='stroke', hue='gender', data=stroke_df_copied, palette=['#FF9999', '#66B2FF'])

plt.title('Figure 5: Number of People Grouped by Stroke and Gender\n')
plt.xlabel('Stroke')
plt.ylabel('Count')

leg = plt.legend(title='Gender', loc='upper left')

for p in ax.patches:
    ax.annotate(format(p.get_height(), '.0f'), 
                 (p.get_x() + p.get_width() / 2., p.get_height()), 
                 ha = 'center', va = 'center', 
                 xytext = (0, 5), 
                 textcoords = 'offset points')

plt.show()


stroke_df_copied = stroke_df.copy()
stroke_df_copied['stroke'] = stroke_df['stroke'].map({0: 'No', 1: 'Yes'})


sns.set_theme(style="whitegrid")

# Define color palette
pal = dict(No="#347EAD", Yes="#C80046")

# Create the bar chart
g = sns.catplot(
    data=stroke_df_copied, kind="count",
    x="smoking_status", hue="stroke",
    palette=pal, alpha=.6, height=6)

# Remove spines
g.despine(left=True)

g.set_axis_labels("Smoking Status", "Number of People")

g.legend.set_title("Stroke")

# Add annotations for label and percentage
total_counts = len(stroke_df_copied)
for ax in g.axes.flat:
    for p in ax.patches:
        x = p.get_x() + p.get_width() / 2
        y = p.get_height()
        ax.annotate(f'{y}\n({y/total_counts:.1%})', (x, y), ha='center', va='bottom', fontsize=10, color='black')

# Add title and title number
plt.title('Figure 6: Comparison of Number of People Who Had Stroke by Smoking Status', y=1.05, fontsize=14)

plt.show()


def create_ridge_plot(df, target, feature, title = None):
    # Initialize the FacetGrid object
    pal = sns.cubehelix_palette(2, rot=-.25, light=.7)
    g = sns.FacetGrid(df, row=target, hue=target, aspect=15, height=2, palette=pal)

    # Draw the densities in a few steps
    g.map(sns.kdeplot, feature,
          bw_adjust=.5, clip_on=False,
          fill=True, alpha=1, linewidth=1.5)
    g.map(sns.kdeplot, feature, clip_on=False, color="w", lw=2, bw_adjust=.5)

    # Add mean line
    def add_mean_line(x, **kwargs):
            mean_val = x.mean()
            plt.axvline(mean_val, color='red', linestyle='--', linewidth=5)  # Change linewidth to 2 for a thicker line
    

    g.map(add_mean_line, feature)

    # Define and use a simple function to label the plot in axes coordinates
    def label(x, color, label):
        ax = plt.gca()
        ax.text(0, .2, label, fontweight="bold", color=color,
                ha="left", va="center", transform=ax.transAxes, fontsize=30)

    g.map(label, feature)

    # Set the subplots to overlap
    g.figure.subplots_adjust(hspace=0)

    if title == None:
        g.set_titles("")
        g.set(yticks=[], ylabel="")
        plt.xticks(fontsize=30)
        plt.xlabel(f"{feature.capitalize()}", fontsize=25) 
        plt.suptitle(f'{feature.capitalize()} Distribution of {target.capitalize()} and No {target.capitalize()}', fontsize=30)
        g.despine(bottom=True, left=True)
    else:
        g.set_titles("")
        g.set(yticks=[], ylabel="")
        plt.xticks(fontsize=30)
        plt.xlabel(f"{feature.capitalize()}", fontsize=25) 
        plt.suptitle(f'{feature.capitalize()} Distribution of {target.capitalize()} and No {target.capitalize()}', fontsize=30, y=1.05)
        plt.title(f'Figure {title}: Distribution of Age, BMI, and Glucose Level on Stroke\n', fontsize=45, y=2.8)
        g.despine(bottom=True, left=True)

 
    plt.show()


create_ridge_plot(stroke_df_copied,"stroke","age", title =7)
create_ridge_plot(stroke_df_copied,"stroke","bmi")
create_ridge_plot(stroke_df_copied,"stroke","avg_glucose_level")


plt.figure(figsize = (15,8))
plt.scatter(stroke_df['avg_glucose_level'], stroke_df['bmi'], alpha = 0.3)
plt.title('Figure 8: Scatter Plot of BMI and Average Glucose Level\n', fontsize = 15)
plt.xlabel('Average glucose level')
plt.ylabel('BMI')
plt.show();


# Assigning colors based on 'stroke' values (1 for red, 0 for green)
colors = stroke_df['stroke'].map({1: '#CD5C5C', 0: '#58D68D'})

plt.figure(figsize=(8, 6))

# Plot green dots first with lower zorder
plt.scatter(stroke_df.loc[stroke_df['stroke'] == 0, 'bmi'], stroke_df.loc[stroke_df['stroke'] == 0, 'avg_glucose_level'], c='#58D68D', alpha=0.7, label='Stroke=No', zorder=2)
# Plot red dots next with higher zorder
plt.scatter(stroke_df.loc[stroke_df['stroke'] == 1, 'bmi'], stroke_df.loc[stroke_df['stroke'] == 1, 'avg_glucose_level'], c='#CD5C5C', alpha=0.7, label='Stroke=Yes', zorder=3)
plt.title('Figure 9: Scatter Plot of BMI vs Average Glucose Level\n')
plt.xlabel('BMI')
plt.ylabel('Average Glucose Level')
plt.legend()
plt.show()


# Count the number of individuals with hypertension, heart disease, and stroke for each marital status
counts = stroke_df.groupby('marital_status')[['hypertension', 'heart_disease', 'stroke']].sum()

# Plot the grouped bar chart with counts and percentages
ax = counts.plot(kind='bar', stacked=False, color = ['#9FE2BF','#CCCCFF','#DE3163'])
plt.title(' Figure 10: Number of Individuals with Hypertension, Heart Disease, and Stroke by Marital Status\n')
plt.xlabel('Marital Status')
plt.ylabel('Count')
plt.legend(['Hypertension', 'Heart Disease', 'Stroke'])

# Add counts on top of each bar
for p in ax.patches:
    ax.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), ha='center', va='center', xytext=(0, 5), textcoords='offset points')

plt.show()


stroke_only = stroke_df[stroke_df['stroke']==1]
pal = dict(male="#347EAD", female="#C80046")

plt.figure(figsize=(15, 8))
ax = sns.boxplot(x=stroke_only['smoking_status'], y=stroke_only['avg_glucose_level'], 
                 hue=stroke_only['gender'], palette=pal,showfliers=False)
sns.swarmplot(x=stroke_only['smoking_status'], y=stroke_only['avg_glucose_level'], 
              hue=stroke_only['gender'], dodge=True, color='grey', size=5)

# Calculate median values for each group
medians = stroke_only.groupby(['smoking_status', 'gender'])['avg_glucose_level'].median()

# Annotate the boxplot with median values
for xtick in ax.get_xticks():
    for hue_tick, hue_color in zip(ax.get_legend().legendHandles, ['#CCFFFF', '#FFCCCC']):  # Colors for the legend
        hue_label = hue_tick.get_label()
        median_val = medians[(ax.get_xticklabels()[xtick].get_text(), hue_label)]
        ax.text(xtick, median_val, f'{median_val:.2f}', 
                ha='center', va='center', color=hue_color, fontweight='bold')

plt.title('Figure 11: Boxplot of Average Glucose Level by Gender and Smoking Status\n', fontsize=14)
plt.show()


def create_implot(df, x, y, col, hue, figureNum=None):
    pal = dict(male="#347EAD", female="#C80046")

    # Show the survival probability as a function of age and sex
    g = sns.lmplot(x=x, y=y, col=col, hue=hue, data=df,
                   palette=pal, y_jitter=.002, logistic=True, truncate=False)
    g.set(xlim=(0, 80), ylim=(-.05, 1.05))
    
    # Add title at the top
    g.fig.suptitle(f'Figure {figureNum}: Impact of {col.capitalize()} and Age on Stroke\n', fontsize=14, y=1.05)


stroke_df_copied = stroke_df.copy()
stroke_df_copied['heart_disease'] = stroke_df['heart_disease'].map({0: 'No', 1: 'Yes'})
stroke_df_copied['hypertension'] = stroke_df['hypertension'].map({0: 'No', 1: 'Yes'})


create_implot(stroke_df_copied,"age","stroke","heart_disease","gender",12)


create_implot(stroke_df_copied,"age","stroke","hypertension","gender",13)

	id	gender	age	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
1665	35432	Female	36.0	Yes	Private	Rural	95.36	25.1	never smoked	0
2859	51746	Female	37.0	Yes	Govt_job	Rural	67.07	27.4	never smoked	0
4192	33886	Female	30.0	No	Private	Rural	87.12	31.1	smokes	0
898	62716	Female	59.0	Yes	Self-employed	Urban	81.64	32.8	Unknown	0
3116	56156	Other	26.0	No	Private	Rural	143.33	22.4	formerly smoked	0
104	33175	Female	57.0	Yes	Govt_job	Urban	110.52	28.5	Unknown	1
1090	61769	Male	30.0	No	Private	Urban	88.65	22.2	never smoked	0
4975	42297	Female	36.0	Yes	Private	Urban	124.31	26.4	Unknown	0
3229	52080	Female	26.0	No	Private	Rural	85.27	24.6	never smoked	0
4779	65892	Female	58.0	Yes	Self-employed	Urban	66.71	51.7	never smoked	0

	count	mean	std	min	25%	50%	75%	max
age	5109.0	43.229986	22.613575	0.08	25.00	45.00	61.00	82.00
hypertension	5109.0	0.097475	0.296633	0.00	0.00	0.00	0.00	1.00
heart_disease	5109.0	0.054022	0.226084	0.00	0.00	0.00	0.00	1.00
avg_glucose_level	5109.0	106.140399	45.285004	55.12	77.24	91.88	114.09	271.74
bmi	4908.0	28.894560	7.854320	10.30	23.50	28.10	33.10	97.60
stroke	5109.0	0.048738	0.215340	0.00	0.00	0.00	0.00	1.00

	count	mean	std	min	25%	50%	75%	max
age	4908.0	42.868810	22.556128	0.08	25.0000	44.00	60.000	82.00
hypertension	4908.0	0.091891	0.288901	0.00	0.0000	0.00	0.000	1.00
heart_disease	4908.0	0.049511	0.216954	0.00	0.0000	0.00	0.000	1.00
avg_glucose_level	4908.0	105.297402	44.425550	55.12	77.0675	91.68	113.495	271.74
bmi	4908.0	28.894560	7.854320	10.30	23.5000	28.10	33.100	97.60
stroke	4908.0	0.042584	0.201937	0.00	0.0000	0.00	0.000	1.00

	count	unique	top	freq
gender	5110	3	female	2994
marital_status	5110	2	yes	3353
work_type	5110	5	private	2925
residence_type	5110	2	urban	2596
smoking_status	5110	4	never smoked	1892

Predicting Stroke Risk ¶

Phase 1: Data Preparation & Visualisation¶

Group Name: 10¶

Name(s) & ID(s) of Group Members:¶

Table of Contents ¶

Introduction ¶

Dataset Source ¶

Dataset Details ¶

Dataset Features ¶

Target Features ¶

Goals and Objectives ¶

Data Cleansing and Preprocessing ¶

Data Cleaning Steps¶

Normalisation of Names and Values ¶

Data Interpretation and Manipulation ¶

Missing Data Imputation ¶

Data Exploration and Visualisation ¶

Univariate Visualisation ¶

Norminal Features¶

Continuous Features¶

Smoking Status¶

Stroke¶

Two-Variable Visualisation ¶

Gender and Stroke¶

Smoking Status and Stroke¶

Other Factors and Stoke¶

Scatter Plot of BMI and Average Glucose Level¶

Three-Variable Visualisation ¶

Scatter Plot of BMI Againts Average Glucose Level with Stroke¶

Bar Chart Comparing the Quantities of Individuals with Hypertension, Heart Disease, and Stroke Based on Marital Status¶

Boxplot Comparing the Average Glucose Level by Gender and Smoking Status¶

Boxplot of average glucose level by gender and smoking status¶

Impact of Heart Disease and Age on Stroke¶

Impact of Hypertension and Age on Stroke¶

Literature Review ¶

Introduction¶

Section 1: Smoking and Stroke¶

Overview¶

Key Finding¶

Limitation¶

Section 2: Gender and Stroke¶

Overview¶

Key Finding¶

Limitation¶

Section 3: Diabetes and Stroke¶

Overview¶

Key Finding¶

Limitation¶

Section 4: HDL-C and Stroke¶

Overview¶

Key Finding¶

Limitation¶

Conclusion¶

Summary and Conclusions ¶

References ¶

Literature Review References ¶

Journal Articles¶

Conferences Papers¶

Other References¶